Advanced Google Cloud LlamaIndex RAG Implementation
RAG is changing how it construct Large Language Model (LLM)-powered apps, but unlike tabular machine learning, where XGBoost is the best, there’s no “go-to” option
Its query_engine is exposed to the ReAct agent as a tool for thinking and acting in Llamaindex to design a ReAct loop
Many techniques exist to direct an LLM to respond to a list of NodeWithScores. Google Cloud may summarize huge nodes before requesting the LLM for a final answer
A Node Post-Processor in Llamaindex implements _postprocess_nodes, which takes the query and list of NodesWithScores as input and produces a new list
The LlamaIndex QueryEngine manages retrieval, node post-processing, and answer synthesis. Passing a retriever, node-post-processing method (if applicable), and response synthesizer as inputs creates a QueryEngine
The LlamaIndex Retriever module abstracts this work well. Subclasses of this module implement the _retrieve function, which accepts a query and returns a list of NodesWithScore
Pre-processing LlamaIndex nodes before embedding for advanced retrieval methods like auto-merging retrieval is possible. The Hierarchical Node Parser groups nodes from a document into a hierarchy